Georgia State University (LAEP Inventory)

At Georgia State University (GSU), predictive analytics have been used to tackle the achievement gap for low income and first-generation students. The university found that students were dropped from courses due to non-payment even when they had high grade point averages (GPAs) and were close to graduation. GSU graduation rate went from 32% in 2003 to 54% in 2014. In the process, the university claimed it removed the achievement gap between students from minority backgrounds or lower socioeconomic status, and their peers who had higher graduation rates. GSU states that it achieved these results by systematically accumulating smaller victories. The university took a series of measures to assist students with costs that were preventing them from staying enrolled in the university. The university used as tutors existing students who were obliged to work for the university as part of their financial aid package. The university also helped students select courses based on predictions of likelihood that they would pass the course.

Classification

Inventory type:

example at scale

Keywords:

predictive analytics

Context of Practice

Learning:

post-compulsory

Geographical:

national: USA

Pedagogic:

This institutional practice relies on information about course grades from historic students, students who are on work-studies, and information about students’ failure to make payment for course fees.

Practical Matters

Tools used:

GSU’s Office of Institutional Research compiled data from multiple siloed systems and created a comprehensive data warehouse

Design and implementation:

By creating Panther Retention Grants, 200 students were given hundreds of dollars to remain enrolled in courses. When students were dropped from a course due to failure to pay course fees, the university examined their GPA and proximity to graduation, and funded those who were most likely to graduate. These grants resulted in many of the recipients going on to graduation. The university expanded the grant programme and worked on cost-cutting measures for students by creating more affordable dorm options.

The university also tackled gate-keeper courses, introductory courses that were good indicators of success for a given major. If a student was performing poorly in a gate-keeper course in their major, the university would hire another student who had a work study agreement and who had previously taken the course to tutor the struggling student.

The university also created an advice system using a database of 2.5 million grades from the past 10 years to advise current students about the courses they would likely succeed in based on their current grades. The same system advises students on what their major could be and saw first-year undeclared majors drop by 40% over two years.

Maturity and Evidence of Utility

The implementation has prompted congressional testimony.

Gate-keeper courses have been researched at a variety of grade levels across primary, secondary, and post-compulsory education.